Case Report: Serial Topography Analysis After Acute Unilateral Diffuse Lamellar Keratitis
Bibliographic record
Abstract
Abstract Significance: Diffuse lamellar keratitis (DLK) is a widely reported complication of laser in-situ keratomileusis (LASIK); however, serial topography tracking the resolution of the condition is sparse. This case illustrates the healing profile which may be expected following an episode of DLK, and the patient reassurances which may be appropriate. Purpose: To report the topography changes and refractive resolution associated with a case of acute unilateral diffuse lamellar keratitis following bilateral femtosecond-assisted hyperopic LASIK. Case Report: A healthy 53-year old male presented with grade two-plus diffuse lamellar keratitis (DLK) 11 days after undergoing successful bilateral wavefront optimized (Alcon, Fort Worth, USA), femtosecond-assisted hyperopic LASIK. Resolution of the DLK was achieved in three weeks with topical corticosteroids. Stabilization of the patient’s topography and refractive error was observed two months after the resolution of the DLK. Conclusions: This case suggests that improvements in corneal topography and refractive error can be expected long after the clinical signs of DLK have subsided. Corneal irregularities and residual refractive errors (usually hyperopia and astigmatism) which exist at the time of resolution on gross examination should be monitored regularly and patients may be reassured that improvements appear likely even after topical regimens have been completed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".